ArticleBMC bioinformatics2020
Fast tree aggregation for consensus hierarchical clustering.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- GLARE: discovering hidden patterns in spaceflight transcriptome using representation learning.NPJ microgravity · 2025Article
- Semi-supervised clustering of quaternion time series: Application to gait analysis in multiple sclerosis using motion sensor data.Statistics in medicine · 2023Article
- Machine learning for data integration in human gut microbiome.Microbial cell factories · 2022Review
- Phenotype clustering in health care: A narrative review for clinicians.Frontiers in artificial intelligence · 2022Review
- A Clonogenic Assay to Quantify Melanoma Micrometastases in Pulmonary Tissue.Methods in molecular biology (Clifton, N.J.) · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundIn unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the past decade. When a single source of data is at play, hierarchical clustering (HC) is extremely popular, as a tree structure is highly interpretable and arguably more informative than just a partition of the data. However, applying blindly HC to multiple sources of data raises computational and interpretation issues.
resultsWe propose mergeTrees, a method that aggregates a set of trees with the same leaves to create a consensus tree. In our consensus tree, a cluster at height h contains the individuals that are in the same cluster for all the trees at height h. The method is exact and proven to be [Formula: see text], n being the individuals and q being the number of trees to aggregate. Our implementation is extremely effective on simulations, allowing us to process many large trees at a time. We also rely on mergeTrees to perform the cluster analysis of two real -omics data sets, introducing a spectral variant as an efficient and robust by-product.
conclusionsOur tree aggregation method can be used in conjunction with hierarchical clustering to perform efficient cluster analysis. This approach was found to be robust to the absence of clustering information in some of the data sets as well as an increased variability within true clusters. The method is implemented in R/C++ and available as an R package named mergeTrees, which makes it easy to integrate in existing or new pipelines in several research areas.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.